CN113704488B - Content generation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure discloses a content generation method, a content generation device, electronic equipment and a storage medium, relates to the technical field of computers, and particularly relates to the technical field of artificial intelligence such as natural language processing, deep learning and knowledge graph. The specific implementation scheme is as follows: acquiring a content generation request, wherein the generation request comprises reference content and a target mode; determining a target generation mode according to the type of the reference content; and processing the reference content based on the target generation mode to generate target content matched with the target mode. Therefore, on the basis of the reference content, the content matched with the target mode can be directly generated, so that the content generation mode is more flexible and intelligent, and the cross-mode content generation can be realized.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to the technical field of artificial intelligence such as natural language processing, deep learning, knowledge graph and the like, and specifically relates to a content generation method, a content generation device, electronic equipment and a storage medium.
Background
As artificial intelligence technology continues to evolve and improve, it has played an extremely important role in various areas related to human daily life, e.g., artificial intelligence technology has made significant progress in the area of content generation. At present, how to more flexibly perform content generation becomes an important research direction.
Disclosure of Invention
The disclosure provides a content generation method, a content generation device, electronic equipment and a storage medium.
According to a first aspect of the present disclosure, there is provided a method of generating content, including:
acquiring a content generation request, wherein the generation request comprises reference content and a target mode;
determining a target generation mode according to the type of the reference content;
and processing the reference content based on the target generation mode to generate target content matched with the target mode.
According to a second aspect of the present disclosure, there is provided a content generating apparatus including:
the acquisition module is used for acquiring a content generation request, wherein the generation request comprises reference content and a target mode;
the first determining module is used for determining a target generation mode according to the type of the reference content;
And the generation module is used for processing the reference content based on the target generation mode so as to generate target content matched with the target mode.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method according to the first aspect.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the method according to the first aspect.
The content generation method, device, electronic equipment and storage medium provided by the disclosure have the following beneficial effects:
in the embodiment of the disclosure, a content generation request is firstly acquired, wherein the generation request comprises reference content and a target mode, then a target generation mode is determined according to the type of the reference content, and finally the reference content is processed based on the target generation mode to generate target content matched with the target mode. Therefore, on the basis of the reference content, the content matched with the target mode can be directly generated, so that the content generation mode is more flexible and intelligent, and the cross-mode content generation can be realized.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow diagram of a method of generating content according to an embodiment of the present disclosure;
FIG. 2 is a flow diagram of a method of generating content according to another embodiment of the present disclosure;
FIG. 3 is a flow diagram of a method of generating content according to yet another embodiment of the present disclosure;
FIG. 4 is a flow diagram of a method of generating content provided in accordance with yet another embodiment of the present disclosure;
FIG. 5 is a flow diagram of a method of generating content according to yet another embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a content generation platform according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram of an algorithm implementation layer in a content generation platform according to an embodiment of the present disclosure;
fig. 8 is a schematic structural view of a content generating apparatus provided according to an embodiment of the present disclosure;
Fig. 9 is a block diagram of an electronic device for implementing a content generation method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The embodiment of the disclosure relates to the technical field of artificial intelligence such as natural language processing, deep learning and the like.
Artificial intelligence (Artificial Intelligence), english is abbreviated AI. It is a new technical science for researching, developing theory, method, technology and application system for simulating, extending and expanding human intelligence.
Deep learning is the inherent regularity and presentation hierarchy of learning sample data, and the information obtained during such learning is helpful in interpreting data such as text, images and sounds. The final goal of deep learning is to enable a machine to analyze learning capabilities like a person, and to recognize text, images, and sound data.
Natural language processing is the processing, understanding, and use of human language (e.g., chinese, english, etc.) by a computer, which is an interdisciplinary of computer science and linguistics, and is often referred to as computational linguistics. Since natural language is the fundamental sign of humans as distinguished from other animals. Without language, human thinking is not talking, so natural language processing embodies the highest tasks and boundaries of artificial intelligence, that is, machines achieve true intelligence only when computers have the ability to process natural language.
The knowledge graph is essentially a semantic network, is a graph-based data structure, and consists of nodes and edges. In the knowledge graph, each node represents an entity existing in the real world, and each side is a relationship between the entities. In popular terms, a knowledge graph is a network of relationships that is obtained by linking together all different kinds of information, and provides the ability to analyze problems from a "relationship" perspective.
Fig. 1 is a flow chart of a method for generating content according to an embodiment of the present disclosure.
It should be noted that, the execution body of the content generation method in this embodiment is a content generation device, and the device may be implemented in software and/or hardware, and the device may be configured in an electronic device, where the electronic device may include, but is not limited to, a terminal, a server, and the like.
As shown in fig. 1, the content generating method includes:
s101: and obtaining a content generation request, wherein the generation request comprises the reference content and the target mode.
Alternatively, the content generation request may be obtained in the event that the user triggers a content generation request control in the content generation platform.
Alternatively, the reference content may be source data or generated content associated with the target content.
Wherein the source data may be material source data for generating the target content. The storage form of the source data may include: simple syndication (Really Simple Syndication, RSS) feeds, word documents, excel data files, and the like, which are not limiting to the present disclosure.
Optionally, in this disclosure, the mode may be a form, a manner, or a style of information transfer, for example, the mode may include graphics, text, images, video, audio, music, audio readings, and so on. Accordingly, the target modality may be a style of the target content. For example, the target modality may be one or more of graphics, images, video, audio books, music, and the like. The present disclosure is not limited in this regard.
The graphics context can be to display target content in a mode of taking texts as cores and taking images as assistance.
Wherein, the video can be divided into short video and long video according to time length. Short video is a video with definite target, single content and short duration. Short video is suitable for filling in the fragmentation time of content consumers. Long video is video with complete structure and details and long duration. Long video is suitable for delivering complex information or complete video art creation, such as recording, movies, etc.
The audio reading material can be data of audio modes with texts as cores and music and sound effects as assistance.
Music is a mode in which emotion is mainly conveyed. Music may be generated from other modalities of content syndication, such as lyric text, music videos, and the like.
Optionally, a style tag may also be included in the generation request. The style tag may be used to determine the style of the target content. For example, style labels may be more lively, concise, popular, etc., which are not limiting to the present disclosure.
S102: and determining a target generation mode according to the type of the reference content.
Alternatively, the target generation pattern may include a non-interactive pattern, and an interactive pattern.
The interaction mode is a mode that a user cooperates with an algorithm to generate target content. In the process of generating the target content, a user can send an interaction instruction to the content generation platform at a proper time based on the content which is generated currently, and then the content generation platform can adopt an artificial intelligence algorithm to complete the requirements of transmission in the interaction instruction. Thus, the interaction mode can improve the user experience and the content generation efficiency, and the quality of the target content.
The non-interactive mode may include a full-automatic mode and a focus mode, among others.
The full-automatic mode is to complete the generation of target content by an artificial intelligence algorithm according to preset source data and flow.
The focus mode is a content generation mode that focuses the target content on a specific, single content. For example, the target content may be a poem, a score, a picture, or the like. In the focusing mode, the content generation platform can automatically complete the initial manuscript of the target content according to the requirement of the user and further assist the user in adjusting the initial manuscript so as to generate the target content.
Alternatively, the target generation mode may be determined to be a non-interactive mode in the case where the reference content is the source data.
Optionally, in a case where the reference content is generated content associated with the target content, the target generation mode is determined to be an interaction mode.
S103: the reference content is processed based on the target generation mode to generate target content matching the target modality.
Alternatively, the target content matching the target modality may be keywords, references, follow-up, etc. The present disclosure is not limited in this regard.
The keywords and the reference materials are keywords, the reference materials and the like of which the content creation platform recommends relevant hot spot directions or scarcity directions for the user based on the current existing content so as to excite the inspiration of the user.
Wherein continuation authoring of the next portion is performed as based on the currently existing content.
Alternatively, the user may determine that the generated target content is a continuation, or is a keyword, reference material, or the like for inspiring inspiration, by triggering a control in the content generation platform.
It should be noted that the content generation platform may use the same model to generate keywords, reference materials, follow-up operations, and so on.
Optionally, if the target mode includes multiple modes, the user may preferentially complete the content of one mode, and then the content generating platform may generate the content of other target modes according to the content of the completed mode.
For example, if the target modality is text, image and video, and the modality of the target content that is preferentially completed is text, then the content generation platform may process the text by using an artificial intelligence algorithm to generate the target content of the image and video modality. Alternatively, the content that is preferably completed is video, and then an artificial intelligence algorithm can be used to automatically supplement the video with subtitles, video special effects or virtual anchor, etc., which is not limited in this disclosure.
It can be appreciated that the modality of the reference content may be the same as or different from the target modality, i.e., the content generation method in the present disclosure may implement cross-modality content generation. For example, the reference content is a text mode, and the generated target content may be a text mode, a graphics-text mode, or a video mode, which is not limited in this disclosure.
In the embodiment of the disclosure, a content generation request is firstly acquired, wherein the generation request comprises reference content and a target mode, then a target generation mode is determined according to the type of the reference content, and finally the reference content is processed based on the target generation mode to generate target content matched with the target mode. Therefore, on the basis of the reference content, the content matched with the target mode can be directly generated, so that the content generation mode is more flexible and intelligent, and the cross-mode content generation can be realized.
Fig. 2 is a flow chart illustrating a method for generating content according to another embodiment of the present disclosure.
As shown in fig. 2, the content generating method includes:
step S201: and obtaining a content generation request, wherein the generation request comprises reference content, a target mode and a triggering condition.
Alternatively, the trigger condition may be a change in the reference content. For example, the trigger condition may be: subscription update, keyword triggering, data change and the like of source data occur; changes may also be made to the generated content associated with the target content. The present disclosure is not limited in this regard.
Step S202: and determining a target generation mode according to the type of the reference content.
Step S203: and processing the reference content based on the target generation mode to generate target content matched with the target mode under the condition that the trigger condition is met.
The specific implementation forms of step S201 to step S203 may refer to the detailed descriptions in other embodiments in the disclosure, and will not be described in detail herein.
Step S204: and inputting the target content into the model generated by training to determine the flow data corresponding to the target content.
Wherein the traffic data may measure the propagation force of the target content. The larger the traffic data, the stronger the propagation force of the target content.
Step S205: and determining the release mode of the target content according to the flow data.
Optionally, the target content may be distributed in multiple dimensions when the flow data corresponding to the target content is greater than a preset threshold, and only one mode is used to distribute the target content when the flow data corresponding to the target content is less than the preset threshold.
For example, when the flow data is greater than a preset threshold, multiple modes such as graphics, video, audio books and the like can be adopted to release the target content simultaneously. And when the flow data is smaller than a preset threshold value, only adopting the image-text mode to release the target content.
It should be noted that the foregoing examples are merely illustrative, and are not intended to be a specific limitation on the release modes in the embodiments of the present disclosure.
Optionally, if the flow data corresponding to the target content is greater than a preset threshold, the target content may be issued preferentially.
Optionally, before publishing the target content, the user may further optimize the content that has been generated according to the optimization suggestions provided by the content generation platform. Such as error correction of text, optimization of resolution of images, clipping of video, etc., which is not limiting in this disclosure.
Optionally, before publishing the target content, the user may also determine the title or the document when the target content is published according to the title, the abstract, the popularization document, and the like recommended by the content generation platform.
In the embodiment of the disclosure, a content generation request is firstly obtained, wherein the generation request comprises a reference content, a target mode and a trigger condition, a target generation mode is determined according to the type of the reference content, then the reference content is processed based on the target generation mode under the condition that the trigger condition is met, so as to generate target content matched with the target mode, and finally, the release mode of the target content is determined according to flow data corresponding to the target content. Therefore, the propagation force of the target is measured through the flow data corresponding to the target content, and the release mode is determined according to the propagation force, so that the content generation mode is more flexible and intelligent, and the quality of the released content is improved.
Fig. 3 is a flow chart illustrating a method for generating content according to still another embodiment of the present disclosure.
As shown in fig. 3, the content generating method includes:
step S301: and obtaining a content generation request, wherein the generation request comprises the identification of the reference content, the target mode and the target template.
The target template is a template for generating target content according to the reference content.
Optionally, the user may record his own authoring process to generate the target template.
Optionally, the user may also modify the reference template provided by the content generation platform according to his own needs, so as to obtain the target template.
The identification of the target template may be a storage address of the target template, or may be a sequence number of the target template, etc., which is not limited in this disclosure.
Step S302: and determining a target generation mode according to the type of the reference content.
The specific implementation manner of step S301 and step S302 may refer to the detailed description of other embodiments in the disclosure, and will not be described in detail herein.
Step S303: and determining the target template according to the identification of the target template.
Step S304: and determining the content to be updated in the target template according to the attribute of each content in the target template.
Optionally, the possible attributes of each content in the target template include fixed variables, automatic decision variables, data variables, and so forth, which are not limited by this disclosure.
Optionally, the content attribute in the target template may be the content of the data variable and the automatic decision variable, and may be determined as the content to be updated.
Step S305: based on the target generation mode, updating the content to be updated according to the reference content to generate target content matched with the target mode.
In the embodiment of the disclosure, a content generation request is firstly obtained, wherein the generation request comprises a reference content, a target mode and an identifier of a target template, then a target generation mode is determined according to the type of the reference content, then the target template is determined according to the identifier of the target template, the content to be updated in the target template is determined according to the attribute of each content in the target template, and finally the content to be updated is updated according to the reference content based on the target generation mode so as to generate target content matched with the target mode. Therefore, the content in the target template is updated according to the reference content, so that the content matched with the target mode can be generated, the content generation mode is more flexible and intelligent, and the content generation efficiency is improved.
Fig. 4 is a flowchart illustrating a method for generating content according to still another embodiment of the present disclosure.
As shown in fig. 4, the content generating method includes:
step S401: and obtaining a content generation request, wherein the generation request comprises the reference content, the target mode and the attribute of the target content.
Wherein the attributes of the target content may include: the type, topic, format, vowel, tone, keyword, etc. of the target content are not limited in this disclosure.
The type of the target content may be poetry, prose, pictures, etc., which is not limited in this disclosure. The format may be long phrase interleaving, etc., which is not limiting to the present disclosure.
Step S402: in the case where the reference content is the source data, the target generation mode is determined to be a non-interactive mode.
The specific implementation manner of step S401 and step S402 may refer to the detailed description of other embodiments in the disclosure, and will not be described in detail herein.
Step S403: and processing the reference content according to the attribute of the target content to generate a plurality of candidate contents matched with the target modality.
Optionally, the content generation platform may process the reference content based on a preset template or sample according to the attribute of the target content, and automatically generate a plurality of candidate contents matched with the target modality.
Optionally, after generating the plurality of candidate contents, style migration may also be performed on the candidate contents according to the style attribute of the target contents.
Step S404: and filtering the plurality of candidate contents according to the similarity between each candidate content and each content in a preset database so as to generate target content matched with the target mode.
The database may contain data similar to the candidate content style and similar to the candidate content style, and may also contain data for reference.
Alternatively, the candidate content having the lowest similarity with each content in the database may be selected as the target content.
Alternatively, candidate contents with the similarity smaller than the threshold value may be obtained from the plurality of candidate contents, and then one candidate content is selected by the user from the candidate contents smaller than the threshold value as the target content.
In the embodiment of the disclosure, a content generation request is first obtained, wherein the generation request includes a reference content, a target modality and attributes of the target content, then, under the condition that the reference content is source data, the target generation mode is determined to be a non-interactive mode, then, the reference content is processed according to the attributes of the target content to generate a plurality of candidate contents matched with the target modality, and finally, the plurality of candidate contents are filtered according to similarity between each candidate content and each content in a preset database to generate the target content matched with the target modality. Therefore, in the non-interactive mode, a plurality of candidate contents matched with the target mode are generated according to the attribute of the preset target content, and then the target content is determined according to the similarity between the candidate contents and the contents in the database, so that the content generation mode is more flexible and intelligent, and the novelty and originality of the generated content are improved.
Fig. 5 is a flowchart illustrating a method for generating content according to still another embodiment of the present disclosure.
As shown in fig. 5, the content generating method includes:
step S501: and obtaining a content generation request, wherein the generation request comprises the reference content and the target mode.
Step S502: in the case where the reference content is generated content associated with the target content, the target generation mode is determined to be an interaction mode.
The specific implementation manner of step S501 and step S502 may refer to the detailed description of other embodiments in the disclosure, and will not be described in detail herein.
Step S503: the reference content is processed to generate and present candidate content matching the target modality.
The candidate content may be a keyword or reference material for stimulating the user's inspiration, or may be a continuation of the next portion generated based on the generated content.
Alternatively, the user may determine that the generated candidate content is a continuation, or is a keyword, reference material, or the like for inspiring inspiration by triggering a control in the content generation platform.
Step S504: and generating target content according to the obtained revising operation aiming at the candidate content.
The revising operation may be to change the style of the candidate content, correct the error of the candidate content, and so on, which is not limited in this disclosure.
Optionally, the user may revise the candidate content by triggering a revision control in the content generation platform, so that the content generation platform revises the candidate content according to the acquired revision operation.
For example, the style of the candidate content may be changed without changing the semantics of the candidate content, making the candidate content more compact, lively or popular. Or if the candidate content is in the text mode, correcting the text; if the candidate content is in the video mode, the subtitle in the video can be corrected.
It should be noted that the above examples are merely illustrative, and are not intended to limit the repair operations in the embodiments of the present disclosure.
Optionally, the user may also define a portion of the existing content, and then the content generation platform revises the existing content according to the obtained revision operation to generate the target content. Such as changing the style of existing content, etc.
In the embodiment of the disclosure, a content generation request is firstly acquired, wherein the generation request comprises a reference content and a target modality, then, under the condition that the reference content is generated content associated with the target content, the target generation mode is determined to be an interaction mode, then, the reference content is processed to generate and display candidate content matched with the target modality, and finally, the target content is generated according to the acquired revision operation for the candidate content. Therefore, in the interaction mode, the generated candidate content matched with the target mode is revised to generate the target content, so that the content generation mode is more flexible and intelligent, and the accuracy of content generation is improved.
Fig. 6 is a schematic diagram of a content generation platform provided by the present disclosure. As shown in fig. 6, the content generation platform includes: platform and service layer, algorithm realization layer, and core technology layer.
Optionally, the platform and service layer may include: platform front end (web page, mobile APP), API interface and platform back end.
Optionally, the core technology layer may include: unified modality and training model, and multi-modality knowledge-graph.
It will be appreciated that in the art of artificial intelligence, and in the art of content generation applications, "multimodal" refers to the integration of multiple modalities of delivering information. Modalities of delivering information may include: text, images, video, speech, music, etc.
The unified mode and training model is a basic model for uniformly processing various single-mode, multi-mode and cross-mode tasks, wherein the content of any mode is converted into vectors in a unified meaning space through different approaches, and then the basic model is subjected to pre-training on large-scale data. The unified mode and training model are the basis for specific algorithm implementation.
The multi-mode knowledge graph can be fused into a pre-training model and algorithm implementation in a knowledge enhancement mode, so that the practical effect of the algorithm is enhanced.
Alternatively, the algorithm implementation layer may include: the system comprises a template-based automatic content creation module, an interactive multimode creation module, a content mode conversion module, a multimode original material generation module, a multimode content understanding and retrieving module and a multimode propagation capability measuring and enhancing module.
The automatic content creation module based on the template can automatically generate target content matched with the target mode by an algorithm based on the target template according to preset reference content. It can be understood that the template-based automatic content creation module is based on a template language with complete descriptive capability, and the template can describe the time sequence of the content and the coordination relationship among the modes, so that the module can automatically generate the target content according to the reference content preset by the user.
The interactive multimode creation module can complete generation of target content according to the interactive instruction of the user. The interactive instruction may be "renew next segment", "rewrite previous segment", "reduce second segment", etc., which is not limited in this disclosure.
The content mode conversion module can convert the same content into different modes. Such as converting an image to video, converting text to an image, etc., which is not limited by this disclosure.
The multimode original material generation module can automatically generate original materials according to the requirements of users. In addition, the module can also perform style migration and conversion on the existing materials to generate original materials.
Wherein the multimode content understanding and retrieving module may generate a tag, a summary, etc. of the target content; error correction checking, color rendering optimization and the like can be performed for the target content; cross-modal retrieval of target content may also be performed, such as retrieving images using text, retrieving videos using images, etc., which is not limiting of the present disclosure.
The multimode propagation capability measuring and enhancing module can measure the propagation capability of the target content and enhance the propagation capability of the target content.
Fig. 7 is a schematic diagram of an algorithm implementation layer in a content generation platform according to an embodiment of the present disclosure. As shown in fig. 7, the template-based automatic content authoring module may include: the system comprises a data source adapter unit, a content composing unit, an automatic creation flow hosting unit and a content publishing interface unit.
The interactive multimodal authoring module may include: the interactive instruction understanding distribution unit, the interactive result screening recommendation unit, the multimode content clipping unit and the multimode content expansion unit.
The multimode originality material generation module may include: an original text generating unit, an original image generating unit, an avatar generating unit, an original music generating unit, a multi-mode style converting unit, and a multi-mode style synthesizing unit.
The multimode content understanding and retrieval module may include: the system comprises a multimode understanding unit, a multimode knowledge association unit, a multimode semantic retrieval unit and a multimode semantic matching unit.
The multimode propagation capability measurement and enhancement module may include: content propagation force prediction unit, and multimode content rendering unit.
In the content modality conversion module, arrows represent conversion relations between content modalities, for example, text may be converted into images, videos, music, and the like. The image may be converted to text, video, and so on. It should be noted that, the conversion between the modes corresponding to each arrow is realized by a corresponding algorithm.
It should be noted that each unit as shown in fig. 7 is implemented by a corresponding specific algorithm.
Fig. 8 is a schematic structural diagram of a content generating apparatus according to an embodiment of the present disclosure.
As shown in fig. 8, the content-based generation apparatus 800 includes: an acquisition module 810, a first determination module 820, a generation module 830.
The obtaining module 810 is configured to obtain a content generation request, where the generation request includes a reference content and a target modality;
a first determining module 820 for determining a target generation mode according to the type of the reference content;
the generating module 830 is configured to process the reference content based on the target generation mode to generate target content that matches the target modality.
In one possible implementation, the first determining module 820 is specifically configured to:
determining that the target generation mode is a non-interactive mode under the condition that the reference content is source data;
in the case where the reference content is generated content associated with the target content, the target generation mode is determined to be an interaction mode.
In one possible implementation manner, the generation request further includes a trigger condition, and the generation module 830 is specifically configured to:
and processing the reference content based on the target generation mode when the trigger condition is satisfied.
In one possible implementation manner, the generating request further includes an identifier of the target template, and the generating module 830 is specifically configured to:
determining a target template according to the identification of the target template;
determining the content to be updated in the target template according to the attribute of each content in the target template;
And according to the reference content, carrying out updating processing on the content to be updated.
In one possible implementation, the target generation mode is an interaction mode, and the generation module 830 is specifically configured to:
processing the reference content to generate and display candidate content matched with the target mode;
and generating target content according to the obtained revising operation aiming at the candidate content.
In one possible implementation, the target generation mode is a non-interactive mode, and the generation request further includes an attribute of the target content, and the generation module 830 is specifically configured to:
processing the reference content according to the attribute of the target content to generate a plurality of candidate contents matched with the target mode;
and filtering the plurality of candidate contents according to the similarity between each candidate content and each content in a preset database so as to generate target content matched with the target mode.
In one possible implementation, the apparatus further includes:
the second determining module is used for inputting the target content into the training generated model so as to determine flow data corresponding to the target content;
and the third determining module is used for determining the release mode of the target content according to the flow data.
It should be noted that the foregoing explanation of the content generation method is also applicable to the content generation apparatus of the present embodiment, and will not be repeated here.
In the embodiment of the disclosure, a content generation request is firstly acquired, wherein the generation request comprises reference content and a target mode, then a target generation mode is determined according to the type of the reference content, and finally the reference content is processed based on the target generation mode to generate target content matched with the target mode. Therefore, on the basis of the reference content, the content matched with the target mode can be directly generated, so that the content generation mode is more flexible and intelligent, and the cross-mode content generation can be realized.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 9 shows a schematic block diagram of an example electronic device 900 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the apparatus 900 includes a computing unit 901 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.
Various components in device 900 are connected to I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, or the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, an optical disk, or the like; and a communication unit 909 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 909 allows the device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunications networks.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the respective methods and processes described above, for example, the content generation method. For example, in some embodiments, the method of generating content may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the above-described content generation method may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the method of generating content in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
In the embodiment of the disclosure, a content generation request is firstly acquired, wherein the generation request comprises reference content and a target mode, then a target generation mode is determined according to the type of the reference content, and finally the reference content is processed based on the target generation mode to generate target content matched with the target mode. Therefore, on the basis of the reference content, the content matched with the target mode can be directly generated, so that the content generation mode is more flexible and intelligent, and the cross-mode content generation can be realized.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (13)
1. A method of content generation, comprising:
acquiring a content generation request, wherein the generation request comprises reference content and a target mode;
determining a target generation mode according to the type of the reference content;
processing the reference content based on the target generation mode to generate target content matched with the target mode;
the target generation mode is an interaction mode, and the processing the reference content based on the target generation mode to generate target content matched with the target mode includes:
processing the reference content to generate and display candidate content matched with the target modality;
generating the target content according to the obtained revising operation aiming at the candidate content;
the target generation mode is a non-interactive mode, the generation request further includes an attribute of target content, and the processing the reference content based on the target generation mode to generate target content matched with the target mode includes:
processing the reference content according to the attribute of the target content to generate a plurality of candidate contents matched with the target modality;
And filtering the plurality of candidate contents according to the similarity between each candidate content and each content in a preset database so as to generate target content matched with the target mode.
2. The method of claim 1, wherein the determining a target generation pattern according to the type of the reference content comprises:
determining that the target generation mode is a non-interactive mode under the condition that the reference content is source data;
in the case where the reference content is generated content associated with a target content, the target generation mode is determined to be an interaction mode.
3. The method of claim 1, wherein the generation request further includes a trigger condition, and the processing the reference content based on the target generation mode includes:
and processing the reference content based on the target generation mode under the condition that the trigger condition is met.
4. The method of claim 1, wherein the generation request further includes an identification of a target template, and the processing the reference content based on the target generation pattern includes:
determining a target template according to the identification of the target template;
Determining the content to be updated in the target template according to the attribute of each content in the target template;
and carrying out updating processing on the content to be updated according to the reference content.
5. The method of any of claims 1-4, wherein after the generating the target content that matches the target modality, further comprising:
inputting the target content into a model generated by training to determine flow data corresponding to the target content;
and determining the release mode of the target content according to the flow data.
6. A content generating apparatus comprising:
the acquisition module is used for acquiring a content generation request, wherein the generation request comprises reference content and a target mode;
the first determining module is used for determining a target generation mode according to the type of the reference content;
the generation module is used for processing the reference content based on the target generation mode to generate target content matched with the target mode;
the target generation mode is an interaction mode, and the generation module is specifically configured to:
processing the reference content to generate and display candidate content matched with the target modality;
Generating the target content according to the obtained revising operation aiming at the candidate content;
the generation request further includes an attribute of the target content, and the generation module is specifically configured to:
processing the reference content according to the attribute of the target content to generate a plurality of candidate contents matched with the target modality;
and filtering the plurality of candidate contents according to the similarity between each candidate content and each content in a preset database so as to generate target content matched with the target mode.
7. The apparatus of claim 6, wherein the first determining module is specifically configured to:
determining that the target generation mode is a non-interactive mode under the condition that the reference content is source data;
in the case where the reference content is generated content associated with a target content, the target generation mode is determined to be an interaction mode.
8. The apparatus of claim 6, wherein the generation request further includes a trigger condition, and the generation module is specifically configured to:
and processing the reference content based on the target generation mode under the condition that the trigger condition is met.
9. The apparatus of claim 6, wherein the generation request further includes an identification of a target template, and the generation module is specifically configured to:
determining a target template according to the identification of the target template;
determining the content to be updated in the target template according to the attribute of each content in the target template;
and carrying out updating processing on the content to be updated according to the reference content.
10. The apparatus of any of claims 6-9, further comprising:
the second determining module is used for inputting the target content into a training generated model so as to determine flow data corresponding to the target content;
and the third determining module is used for determining the release mode of the target content according to the flow data.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product comprising computer instructions which, when executed by a processor, implement the steps of the method of claim 1.
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